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X-WR-CALNAME;VALUE=TEXT:ĢĒŠÄŌ­““
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DTSTART:20171105T020000
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UID:calendar.289091.field_event_date.0@www.wright.edu
DTSTAMP:20260220T001003Z
CREATED:20180206T214339Z
DESCRIPTION:Committee:Ā  Drs. Tanvi Banerjee\, TK Prasad\, and Michelle Chea
 thamABSTRACT:In recent times\, social media platforms like Twitter have be
 come more popular and people have become more interactive and responsive t
 han before. People often react to every news in real-time and within no-ti
 me\, the information spreads rapidly. Even with viral diseases like Zika\,
  people tend to share their opinions and concerns on social media. This ca
 n be leveraged by the health officials to track the disease in real-time t
 hereby reducing the time lag due to traditional surveys. A faster and accu
 rate detection of the disease can allow health officials to understand peo
 pleā€™s opinion of the disease and take necessary precautions to prevent the
  misinformation from spreading at a faster pace.The purpose of this study 
 was to analyze the tweets to understand the public opinion on Zika virus. 
 With the help of machine learning and natural language processing\, we cla
 ssify the tweets into four disease characteristics namely\, Symptom\, Prev
 ention\, Transmission\, and Treatment.Ā  Once the tweets were classified\, 
 topic modeling was performed using Latent Dirichlet Allocation (LDA) to ge
 nerate underlying patterns within each disease characteristics. Such analy
 sis can help to gain a deeper understanding of the content of tweets perta
 ining to Zika.Ā 
DTSTART;TZID=America/New_York:20180215T120000
DTEND;TZID=America/New_York:20180215T140000
LAST-MODIFIED:20180206T220612Z
LOCATION:304 Russ Engineering Center
SUMMARY:Masters Thesis Defense ā€œA Twitter-Based Study for Understanding Pub
 lic Reaction on Zika Virusā€ By Roop Teja Muppalla
URL;TYPE=URI:/events/masters-thesis-defense-%E2%80%9C
 -twitter-based-study-understanding-public-reaction-zika-virus%E2%80%9D-roo
 p
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